Transferable Time-Series Forecasting under Causal Conditional Shift

Zijian Li, Ruichu Cai, Zhifeng Hao, Kun Zhang

Abstract:

This paper focuses on the problem of semi-supervised domain adaptation for time-series forecasting, which is
underexplored in literatures, despite being often encountered in practice. Existing methods on time-series domain adaptation mainly
follow the paradigm designed for the static data, which cannot handle domain-specific complex conditional dependencies raised by
data offset, time lags, and variant data distributions. In order to address these challenges, we analyze variational conditional
dependencies in time-series data and find that the causal structures are usually stable among domains, and further raise the causal
conditional shift assumption. Enlightened by this assumption, we consider the causal generation process for time-series data and
propose an end-to-end model for the semi-supervised domain adaptation problem on time-series forecasting. Our method can not only
discover the Granger-Causal structures among cross-domain data but also address the cross-domain time-series forecasting problem
with accurate and interpretable predicted results. We further theoretically analyze the superiority of the proposed method, where the
generalization error on the target domain is bounded by the empirical risks and by the discrepancy between the causal structures from
different domains. Experimental results on both synthetic and real data demonstrate the effectiveness of our method for the
semi-supervised domain adaptation method on time-series forecasting.